Neural computing-based pedestrian detection from image sequence

Preventing a traffic accident is a good way to solve many problems in the world for the current generation surrounding with many automotive technologies causing many people´s death from the accident. The prevention makes an important impact to every society for making many people more safety and improving their lives´ quality. In the fact, the primary cause is mostly drivers´ carelessness and lacking of control, which might be because of addiction of drugs, resulting that a pedestrian walking on a road pains and then becomes dead. Therefore the problem can be solved using a computer for analysis and making a decision as a human called pedestrian detection. This study uses many several enhancement algorithms and processes for detecting integrated with an analysis based on a neural computing to decide whether a detected object is a pedestrian. Moreover the study shows an experimental result that the procedure is sufficient and efficient to be a fundamental knowledge for the next related work including a real-life traffic situation.